Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this stud...Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three dif- ferent techniques： error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coeffi- cients （K）. The average overall accuracies for rabi and kharif are 82.83% and 78.21%, re- spectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation be- tween cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.展开更多
Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, seve...Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. This research includes two sections (1) Landuse/Landcover (LULC) classification and (2) accuracy assessment. In this study supervised classification was performed using Non Parametric Rule. The major LULC classified were agriculture (65.0%), water body (4.0%), and built up areas (18.3%), mixed forest (5.2%), shrubs (7.0%), and Barren/bare land (0.5%). The study had an overall classification accuracy of 81.7% and kappa coefficient (K) of 0.722. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment.展开更多
Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different cro...Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.展开更多
Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classif...Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.展开更多
Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact ...Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact on the change of ecosystem.The primary goal of this study is to determine the impacts of LULC changes on ecosystem service values(ESVs)in the upper Gilgel Abbay watershed,Ethiopia.Changes in LULC types were studied using three Landsat images representing 1986,2003,and 2021.The Landsat images were classified using a supervised image classification technique in Earth Resources Data Analysis System(ERDAS)Imagine 2014.We classified ESs in this study into four categories(including provisioning,regulating,supporting,and cultural services)based on global ES classification scheme.The adjusted ESV coefficient benefit approach was employed to measure the impacts of LULC changes on ESVs.Five LULC types were identified in this study,including cultivated land,forest,shrubland,grassland,and water body.The result revealed that the area of cultivated land accounted for 64.50%,71.50%,and 61.50%of the total area in 1986,2003,and 2021,respectively.The percentage of the total area covered by forest was 9.50%,5.90%,and 14.80%in 1986,2003,and 2021,respectively.Result revealed that the total ESV decreased from 7.42×10^(7) to 6.44×10^(7) USD between 1986 and 2003.This is due to the expansion of cultivated land at the expense of forest and shrubland.However,the total ESV increased from 6.44×10^(7) to 7.76×10^(7) USD during 2003-2021,because of the increment of forest and shrubland.The expansion of cultivated land and the reductions of forest and shrubland reduced most individual ESs during 1986-2003.Nevertheless,the increase in forest and shrubland at the expense of cultivated land enhanced many ESs during 2003-2021.Therefore,the findings suggest that appropriate land use practices should be scaled-up to sustainably maintain ESs.展开更多
Chalet farming,as a specific type of agricultural landscape management,has been established in many European mountain ranges,including the Krkono?e Mountains and the Hruby Jeseník Mountains in Czechia.During the ...Chalet farming,as a specific type of agricultural landscape management,has been established in many European mountain ranges,including the Krkono?e Mountains and the Hruby Jeseník Mountains in Czechia.During the operation of such farming from 16/17th century till 1945,many changes in land use/land cover and landscape at all occurred,which are generally evaluated positively.Turbulent events including political,economic and social changes and the displacement of the German-speaking population associated with them in the mid-20th century rapidly ended this development,causing significant landscape changes,such as the abandonment of agricultural land and succession,afforestation,expansion of the alpine tree line,reduction of diversity.The aim of our study is to evaluate changes of land cover(forests,dwarf pine,grasslands,other areas)from 1936/1946 till 2021,secondary succession and driving forces of change for selected meadow enclaves in the Krkonose Mountains and the Hruby Jeseník Mountains after the decline of mountain chalet farming since the middle of 20th century.We used remote sensing methods(aerial imagery)and field research(dendrochronology and comparative photography)to detect the land use/land cover changes in the selected study areas in the Krkono?e Mountains and the Hruby Jeseník Mountains.We documented the process of the succession,which occurred almost immediately after the end of farming,peaking about 10–20 years later,with an earlier start in the Hruby Jeseník Mountains.The succession led to the significant change of land use/land cover and these processes were similar in both mountain ranges.The largest changes were a decrease in grasslands by 62%–64%and an increase in forest area by 33%–40%for both study areas.The abandonment of land is the main consequence of a crucial political driving forces(displacement of German-speaking population)in the Krkono?e Mountains and the Hruby Jeseník Mountains.展开更多
Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development,accelerating the achievement of the"dual carbon"goals,and promoting the high-quality ...Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development,accelerating the achievement of the"dual carbon"goals,and promoting the high-quality development of ecological environment.The carbon sequestration capacity within the mountain-desert-oasis system(MDOS),a unique landscape pattern,exhibits significant gradient characteristics,and its carbon sink potential can be substantially improved through multi-scale spatial optimization.This study employed the Integrated Valuation of Ecosystem Services and Tradeoff(InVEST)model to estimate carbon storage and sequestration(CSS)in the Gansu section of Heihe River Basin,China,a representative MDOS,based on land use/land cover(LULC)data from 1990 to 2020.The Patch-level Land Use Simulation(PLUS)model was coupled to simulate LULC and estimate carrying CSS under natural development(ND),ecological protection(EP),water constraint(WC),and economic development(ED)scenarios for 2035.Furthermore,the study constructed and optimized the CSS pattern on the basis of economic and ecological benefits,exploring the guiding significance of different scenarios for pattern optimization.The results showed that CSS spatial distribution is closely correlated with LULC pattern,and CSS is expected to improve in the future.CSS showed an overall increase across subsystems during 1990–2020,but varied across LULC types.CSS of construction land in all subsystems exhibited an increasing trend,while CSS of unused land showed a decreasing trend,with specific changes of 1.68×103 and 3.43×105 t,respectively.Regional CSS dynamics were mainly driven by conversions among unused land,cultivated land,and grassland.The CSS pattern of MDOS was divided into carbon sink functional region(CSFR),low carbon conservation region(LCCR),low carbon economic region(LCER),and economic development region(EDR).Water resources coordination served as the basis of pattern optimization,while the four dimensions—ecological carbon sink,low-carbon maintenance,agricultural carbon reduction and sink enhancement,and urban carbon emission reduction—framed the optimization framework.ND,EP,WC,and ED scenarios provided guidance as the basic reference,optimal benefit,"dual carbon"baseline,and upper development limit,respectively.Additionally,the detailed CSS sub-partitions of MDOS covered most potential scenarios of such ecosystems,demonstrating the applicability of these sub-partitions.These findings provide valuable references for enhancing CSS and hold important significance for low-carbon territorial spatial planning in the MDOS.展开更多
Carbon storage serves as a key indicator of ecosystem services and plays a vital role in maintaining the global carbon balance.Land use and cover change(LUCC)is one of the primary drivers influencing carbon storage va...Carbon storage serves as a key indicator of ecosystem services and plays a vital role in maintaining the global carbon balance.Land use and cover change(LUCC)is one of the primary drivers influencing carbon storage variations in terrestrial ecosystems.Therefore,evaluating the impacts of LUCC on carbon storage is crucial for achieving strategic goals such as the China’s dual carbon goals(including carbon peaking and carbon neutrality).This study focuses on the Aral Irrigation Area in Xinjiang Uygur Autonomous Region,China,to assess the impacts of LUCC on regional carbon storage and their spatiotemporal dynamics.A comprehensive LUCC database from 2000 to 2020 was developed using Landsat satellite imagery and the random forest classification algorithm.The integrated valuation of ecosystem services and trade-offs(InVEST)model was applied to quantify carbon storage and analyze its response to LUCC.Additionally,future LUCC patterns for 2030 were projected under multiple development scenarios using the patch-generating land use simulation(PLUS)model.These future LUCC scenarios were integrated with the InVEST model to simulate carbon storage trends under different land management pathways.Between 2000 and 2020,the dominant land use types in the study area were cropland(area proportion of 35.52%),unused land(34.80%),and orchard land(12.19%).The conversion of unused land and orchard land significantly expanded the area of cropland,which increased by 115,742.55 hm^(2).During this period,total carbon storage and carbon density increased by 7.87×10^(6) Mg C and 20.19 Mg C/hm^(2),respectively.The primary driver of this increase was the conversion of unused land into cropland,accounting for 49.28%of the total carbon storage gain.Carbon storage was notably lower along the northeastern and southeastern edges.By 2030,the projected carbon storage is expected to increase by 0.99×10^(6),1.55×10^(6),and 1.71×10^(6) Mg C under the natural development,cropland protection,and ecological conservation scenarios,respectively.In contrast,under the urban development scenario,carbon storage is projected to decline by 0.40×10^(6) Mg C.In line with China’s dual carbon goals,the ecological conservation scenario emerges as the most effective strategy for enhancing carbon storage.Accordingly,strict enforcement of the cropland red line is recommended.This study provides a valuable scientific foundation for regional ecosystem restoration and sustainable development in arid regions.展开更多
The Liaohe River Basin(LRB)in Northeast China,a critical agricultural and industrial zone,has faced escalating water resource pressures in recent decades due to rapid urbanization,intensified land use changes,and clim...The Liaohe River Basin(LRB)in Northeast China,a critical agricultural and industrial zone,has faced escalating water resource pressures in recent decades due to rapid urbanization,intensified land use changes,and climate variability.Understanding the spatiotemporal dynamics of water yield and its driving factors is essential for sustainable water resource management in this ecologically sensitive region.This study employed the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model to quantify the spatiotemporal patterns of water yield in the LRB(dividing into six sub-basins from east to west:East Liaohe River Basin(ELRB),Taizi River Basin(TRB),Middle Liaohe River Basin(MLRB),West Liaohe River Basin(WLRB),Xinkai River Basin(XRB),and Wulijimuren River Basin(WRB))from 1993 to 2022,with a focus on the impacts of climate change and land use cover change(LUCC).Results revealed that the LRB had an average annual precipitation of 483.15 mm,with an average annual water yield of 247.54 mm,both showing significant upward trend over the 30-a period.Spatially,water yield demonstrated significant heterogeneity,with higher values in southeastern sub-basins and lower values in northwestern sub-basins.The TRB exhibited the highest water yield due to abundant precipitation and favorable topography,while the WRB recorded the lowest water yield owing to arid conditions and sparse vegetation.Precipitation played a significant role in shaping the annual fluctuations and total volume of water yield,with its variability exerting substantially greater impacts than actual evapotranspiration(AET)and LUCC.However,LUCC,particularly cultivated land expansion and grassland reduction,significantly reshaped the spatial distribution of water yield by modifying surface runoff and infiltration patterns.This study provides critical insights into the spatiotemporal dynamics of water yield in the LRB,emphasizing the synergistic effects of climate change and land use change,which are pivotal for optimizing water resource management and advancing regional ecological conservation.展开更多
Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classificatio...Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classification(SPLC)method combined with a fusion model(flexible spatiotemporal data fusion,FSDAF)(abbreviated as SPLC-F)was proposed to map multi-year land cover and crop type(LC-CT)distribution in agricultural irrigated areas with complex landscapes and cropping system,using time series optical images(Landsat and MODIS).The SPLC-F method was well validated and applied in a super-large irrigated area(Hetao)of the upper Yellow River Basin(YRB).Results showed that the SPLC-F method had a satisfactory performance in producing long-term LC-CT maps in Hetao,without the requirement of field sampling.Then,the spatio-temporal variation and the driving factors of the cropping systems were further analyzed with the aid of detailed household surveys and statistics.We clarified that irrigation and salinity conditions were the main factors that had impacts on crop spatial distribution in the upper YRB.Investment costs,market demand,and crop price are the main driving factors in determining the temporal variations in cropping distribution.Overall,this study provided essential multi-year LC-CT maps for sustainable management of agriculture,eco-environments,and food security in the upper YRB.展开更多
Terrain and geological formation are crucial natural environmental factors that constrain land use and land cover changes.Studying their regulatory role in regional land use and land cover changes is significant for g...Terrain and geological formation are crucial natural environmental factors that constrain land use and land cover changes.Studying their regulatory role in regional land use and land cover changes is significant for guiding regional land resource management.Taking the Danjiang River Basin in the Qinling Mountains of China as an example,this paper incorporates terrain(elevation,slope,and aspect)factors and geological formation to comprehensively analyse the differentiation characteristics of land use spatial patterns based on the examination of land use changes in 2000,2010,and 2020.Moreover,the geographical detector is employed to compare and analyse the effect of each factor on the spatial heterogeneity of land use.The results show that:(1)From 2000 to 2020,the areas of arable land and forestland in the Danjiang River Basin decreased while the areas of grassland,water areas,construction land,and unused land continuously increased.The comprehensive land use dynamics index was+0.09%,indicating a generally low level of land development.(2)Differences in the natural environmental factors of terrain and geological formation have a significant controlling effect on the spatial heterogeneity of land use.Specifically,there are notable differences in the advantageous distribution characteristics of various land use types across different levels of influencing factors.(3)The factor detection results reveal that geological formation has the strongest influence on the spatial heterogeneity of land use,followed by elevation and slope while aspect has the weakest influence.After the interaction among the factors,they nonlinearly enhance the explanation of spatial heterogeneity in land use.Overall,the influence of geological formation on the spatial heterogeneity of land use is greater than that of terrain factors.This study provides new geological evidence for natural resource management departments to conduct regional spatial planning,ecological and environmental protection and restoration,and land structure optimization and adjustment.展开更多
Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and ...Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.展开更多
Cloud cover constitutes a major obstacle to land cover classification in the humid tropical regions when using optical remote sensing such as Landsat imagery. The advent of freely available Sentinel-1 C band synthetic...Cloud cover constitutes a major obstacle to land cover classification in the humid tropical regions when using optical remote sensing such as Landsat imagery. The advent of freely available Sentinel-1 C band synthetic aperture radar (SAR) imagery offers new opportunities for land cover classification in frequently cloud covered environments. In this study, we investigated the utility of Sentinel-1 for extracting land use land cover (LULC) information in the coastal low lying strip of Douala, Cameroon when compared with Landsat enhanced thematic mapper (TM). We also assessed the potential of integrating Sentinel-1 and Landsat. The major LULC classes in the region included water, settlement, bare ground, dark mangroves, green mangroves, swampy vegetation, rubber, coastal forest and other vegetation and palms. Textural variables including mean, correlation, contrast and entropy were derived from the Sentinel-1 C band. Various conventional image processing techniques and the support vector machine (SVM) algorithm were applied. Only four land cover classes (settlement, water, mangroves and other vegetation and rubber) could be calibrated and validated using SAR imagery due to speckles. The Sentinel-1 only classification yielded a lower overall classification accuracy (67.65% when compared to all Landsat bands (88.7%)). The integrated Sentinel-1 and Landsat data showed no significant differences in overall accuracy assessment (88.71% and 88.59%, respectively). The three best spectral bands (5, 6, 7) of Landsat imagery yielded the highest overall accuracy assessment (91.96%). in the study. These results demonstrate a lower potential of Sentinel-1 for land cover classification in the Douala estuary when compared with cloud free Landsat images. However, comparable results were obtained when only broad classes were considered.展开更多
Riparian land use/land cover(LULC)plays a crucial role in maintaining riverine water quality by altering the transport of pollutants and nutrients.Nevertheless,establishing a direct relationship between water quality ...Riparian land use/land cover(LULC)plays a crucial role in maintaining riverine water quality by altering the transport of pollutants and nutrients.Nevertheless,establishing a direct relationship between water quality and LULC is challenging due to the multi-indicator nature of both factors.Water quality encompasses a multitude of physical,chemical,and biological parameters,while LULC represents a diverse array of land use types.Riparian habitat quality(RHQ)serves as an indicator of LULC.Yet,it remains to be seen whether RHQ can act as a proxy of LULC for assessing the impact of LULC on riverine water quality.This study examines the interplay between RHQ,LULC and water quality,and develops a comprehensive indicator to predict water quality.We measured several water quality parameters,including pH(potential of hydrogen),TN(total nitrogen),TP(total phosphorus),T_(water)(water temperature),DO(dissolved oxygen),and EC(electrical conductivity)of the Yue and Jinshui Rivers draining to the Han River during 2016,2017 and 2018.The water quality index(WQI)was further calculated.RHQ is assessed by the InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)model.Our study found noticeable seasonal differences in water quality,with a higher WQI observed in the dry season.The RHQ was strongly correlated with LULC compositions.RHQ positively correlated with WQI,and DO concentration and vegetation land were negatively correlated with T_(water),TN,TP,EC,cropland,and construction land.These correlations were stronger in the rainy season.Human-dominated land,such as construction land and cropland,significantly contributed to water quality degradation,whereas vegetation promoted water quality.Regression models showed that the RHQ explained variations in WQI better than LULC types.Our study concludes that RHQ is a new and comprehensive indicator for predicting the dynamics of riverine water quality.展开更多
The Turpan-Hami(Tuha)Basin in Xinjiang Uygur Autonomous Region of China,holds significant strategic importance as a key economic artery of the ancient Silk Road and the Belt and Road Initiative,necessitating a holisti...The Turpan-Hami(Tuha)Basin in Xinjiang Uygur Autonomous Region of China,holds significant strategic importance as a key economic artery of the ancient Silk Road and the Belt and Road Initiative,necessitating a holistic understanding of the spatiotemporal evolution of land use/land cover(LULC)to foster sustainable planning that is tailored to the region's unique resource endowments.However,existing LULC classification methods demonstrate inadequate accuracy,hindering effective regional planning.In this study,we established a two-level LULC classification system(8 primary types and 22 secondary types)for the Tuha Basin.By employing Landsat 5/7/8 imagery at 5-a intervals,we developed the LULC dataset of the Tuha Basin from 1990 to 2020,conducted the accuracy assessment and spatiotemporal evolution analysis,and simulated the future LULC under various scenarios via the Markov-Future Land Use Simulation(Markov-FLUS)model.The results revealed that the average overall accuracy values of our LULC dataset were 0.917 and 0.864 for the primary types and secondary types,respectively.Compared with the seven mainstream LULC products(GlobeLand30,Global 30-meter Land Cover with Fine Classification System(GLC_FCS30),Finer Resolution Observation and Monitoring of Global Land Cover PLUS(FROM_GLC PLUS),ESA Global Land Cover(ESA_LC),Esri Land Cover(ESRI_LC),China Multi-Period Land Use Land Cover Change Remote Sensing Monitoring Dataset(CNLUCC),and China Annual Land Cover Dataset(CLCD))in 2020,our LULC data exhibited dramatically elevated overall accuracy and provided more precise delineations for land features,thereby yielding high-quality data backups for land resource analyses within the basin.In 2020,unused land(78.0%of the study area)and grassland(18.6%)were the dominant LULC types of the basin;although cropland and construction land constituted less than 1.0%of the total area,they played a vital role in arid land development and primarily situated within oases that form the urban cores of the cities of Turpan and Hami.Between 1990 and 2020,cropland and construction land exhibited a rapid expansion,and the total area of water body decreased yet resurging after 2015 due to an increase in areas of reservoir and pond.In future scenario simulations,significant increases in areas of construction land and cropland are anticipated under the business-as-usual scenario,whereas the wetland area will decrease,suggesting the need for ecological attention under this development pathway.In contrast,the economic development scenario underscores the fast-paced expansion of construction land,primarily from the conversion of unused land,highlighting the significant developmental potential of unused land with a slowing increase in cropland.Special attention should thus be directed toward ecological and cropland protection during development.This study provides data supports and policy recommendations for the sustainable development goals of Tuha Basin and other similar arid areas.展开更多
Land use/land cover represents the interactive and comprehensive influences between human activities and natural conditions,leading to potential conflicts among natural and human-related issues as well as among stakeh...Land use/land cover represents the interactive and comprehensive influences between human activities and natural conditions,leading to potential conflicts among natural and human-related issues as well as among stakeholders.This study introduced economic standards for farmers.A hybrid approach(CA-ABM)of cellular automaton(CA)and an agent-based model(ABM)was developed to effectively deal with social and land-use synergic issues to examine human–environment interactions and projections of land-use conversions for a humid basin in south China.Natural attributes and socioeconomic data were used to analyze land use/land cover and its drivers of change.The major modules of the CA-ABM are initialization,migration,assets,land suitability,and land-use change decisions.Empirical estimates of the factors influencing the urban land-use conversion probability were captured using parameters based on a spatial logistic regression(SLR)model.Simultaneously,multicriteria evaluation(MCE)and Markov models were introduced to obtain empirical estimates of the factors affecting the probability of ecological land conversion.An agent-based CA-SLR-MCE-Markov(ABCSMM)land-use conversion model was proposed to explore the impacts of policies on land-use conversion.This model can reproduce observed land-use patterns and provide links for forest transition and urban expansion to land-use decisions and ecosystem services.The results demonstrated land-use simulations under multi-policy scenarios,revealing the usefulness of the model for normative research on land-use management.展开更多
With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to th...With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.展开更多
As the global population continues to expand,the demand for natural resources increases.Unfortunately,human activities account for 23%of greenhouse gas emissions.On a positive note,remote sensing technologies have eme...As the global population continues to expand,the demand for natural resources increases.Unfortunately,human activities account for 23%of greenhouse gas emissions.On a positive note,remote sensing technologies have emerged as a valuable tool in managing our environment.These technologies allow us to monitor land use,plan urban areas,and drive advancements in areas such as agriculture,climate changemitigation,disaster recovery,and environmentalmonitoring.Recent advances in Artificial Intelligence(AI),computer vision,and earth observation data have enabled unprecedented accuracy in land use mapping.By using transfer learning and fine-tuning with red-green-blue(RGB)bands,we achieved an impressive 99.19%accuracy in land use analysis.Such findings can be used to inform conservation and urban planning policies.展开更多
The Himalayan region has been experiencing stark impacts of climate change,demographic and livelihood pattern changes.The analysis of land use and land cover(LULC)change provides insights into the shifts in spatial an...The Himalayan region has been experiencing stark impacts of climate change,demographic and livelihood pattern changes.The analysis of land use and land cover(LULC)change provides insights into the shifts in spatial and temporal patterns of landscape.These changes are the combined effects of anthropogenic and natural/climatic factors.The present study attempts to monitor and comprehend the main drivers behind LULC changes(1999-2021)in the Himalayan region of Pithoragarh district,Uttarakhand.Pithoragarh district is a border district,remotely located in the north-east region of Uttarakhand,India.The study draws upon primary and secondary data sources.A total of 400 household surveys and five group discussions from 38 villages were conducted randomly to understand the climate perception of the local community and the drivers of change.Satellite imagery,CRU(Climatic Research Unit)climate data and climate perception data from the field have been used to comprehensively comprehend,analyze,and discuss the trends and reasons for LULC change.GIS and remote sensing techniques were used to construct LULC maps.This multifaceted approach ensures comprehensive and corroborated information.Five classes were identified and formed viz-cultivation,barren,settlement,snow,and vegetation.Results show that vegetation and builtup have increased whereas cultivation,barren land,and snow cover have decreased.The study further aims to elucidate the causes behind LULC changes in the spatially heterogeneous region,distinguishing between those attributed to human activities,climate shifts,and the interconnected impacts of both.The study provides a comprehensive picture of the study area and delivers a targeted understanding of local drivers and their potential remedies by offering a foundation for formulating sustainable adaptation policies in the region.展开更多
Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and...Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and transfer rate of LULC in the Jinghe River Basin(JRB),China using LULC data from 2000 to 2020.Through trajectory analysis,knowledge maps,chord diagrams,and standard deviation ellipse method,we examined the spatiotemporal characteristics of LULC changes.We further established an index system encompassing natural factors(digital elevation model(DEM),slope,aspect,and curvature),socio-economic factors(gross domestic product(GDP)and population),and accessibility factors(distance from railways,distance from highways,distance from water,and distance from residents)to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector(Geodetector).The key findings indicate that from 2000 to 2020,the JRB experienced significant LULC changes,particularly for farmland,forest,and grassland.During the study period,LULC change trajectories were categorized into stable,early-stage,late-stage,repeated,and continuous change types.Besides the stable change type,the late-stage change type predominated the LULC change trajectories,comprising 83.31% of the total change area.The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010.The LULC changes exhibited a discrete spatial expansion trend during 2000-2020,predominantly extending from southeast to northwest of the JRB.Influential driving factors on LULC changes included slope,GDP,and distance from highways.The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020.This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.展开更多
文摘Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three dif- ferent techniques： error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coeffi- cients （K）. The average overall accuracies for rabi and kharif are 82.83% and 78.21%, re- spectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation be- tween cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.
文摘Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. This research includes two sections (1) Landuse/Landcover (LULC) classification and (2) accuracy assessment. In this study supervised classification was performed using Non Parametric Rule. The major LULC classified were agriculture (65.0%), water body (4.0%), and built up areas (18.3%), mixed forest (5.2%), shrubs (7.0%), and Barren/bare land (0.5%). The study had an overall classification accuracy of 81.7% and kappa coefficient (K) of 0.722. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment.
基金supported by the Deanship of Research and Graduate Studies at the King Khalid University(RGP2/287/46)the Princess Nourah bint Abdulrahman University Researchers Supporting Project(PNURSP2025R733)+1 种基金the Princess Nourah bint Abdulrahman University Research Supporting Project(RSPD2025R787)the King Saud University,Saudi Arabia.
文摘Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.
基金supported by the National Natural Science Foundation of China[grant number 41930104]by the Research Grants Council of Hong Kong[grant number PolyU 152219/18E].
文摘Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.
文摘Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact on the change of ecosystem.The primary goal of this study is to determine the impacts of LULC changes on ecosystem service values(ESVs)in the upper Gilgel Abbay watershed,Ethiopia.Changes in LULC types were studied using three Landsat images representing 1986,2003,and 2021.The Landsat images were classified using a supervised image classification technique in Earth Resources Data Analysis System(ERDAS)Imagine 2014.We classified ESs in this study into four categories(including provisioning,regulating,supporting,and cultural services)based on global ES classification scheme.The adjusted ESV coefficient benefit approach was employed to measure the impacts of LULC changes on ESVs.Five LULC types were identified in this study,including cultivated land,forest,shrubland,grassland,and water body.The result revealed that the area of cultivated land accounted for 64.50%,71.50%,and 61.50%of the total area in 1986,2003,and 2021,respectively.The percentage of the total area covered by forest was 9.50%,5.90%,and 14.80%in 1986,2003,and 2021,respectively.Result revealed that the total ESV decreased from 7.42×10^(7) to 6.44×10^(7) USD between 1986 and 2003.This is due to the expansion of cultivated land at the expense of forest and shrubland.However,the total ESV increased from 6.44×10^(7) to 7.76×10^(7) USD during 2003-2021,because of the increment of forest and shrubland.The expansion of cultivated land and the reductions of forest and shrubland reduced most individual ESs during 1986-2003.Nevertheless,the increase in forest and shrubland at the expense of cultivated land enhanced many ESs during 2003-2021.Therefore,the findings suggest that appropriate land use practices should be scaled-up to sustainably maintain ESs.
基金funded by the European Commission,CINEA Horizon Europe project no.101081307“Towards Sustainable Land-Use in the Context of Climate Change and Biodiversity in Europe(Europe-LAND)”。
文摘Chalet farming,as a specific type of agricultural landscape management,has been established in many European mountain ranges,including the Krkono?e Mountains and the Hruby Jeseník Mountains in Czechia.During the operation of such farming from 16/17th century till 1945,many changes in land use/land cover and landscape at all occurred,which are generally evaluated positively.Turbulent events including political,economic and social changes and the displacement of the German-speaking population associated with them in the mid-20th century rapidly ended this development,causing significant landscape changes,such as the abandonment of agricultural land and succession,afforestation,expansion of the alpine tree line,reduction of diversity.The aim of our study is to evaluate changes of land cover(forests,dwarf pine,grasslands,other areas)from 1936/1946 till 2021,secondary succession and driving forces of change for selected meadow enclaves in the Krkonose Mountains and the Hruby Jeseník Mountains after the decline of mountain chalet farming since the middle of 20th century.We used remote sensing methods(aerial imagery)and field research(dendrochronology and comparative photography)to detect the land use/land cover changes in the selected study areas in the Krkono?e Mountains and the Hruby Jeseník Mountains.We documented the process of the succession,which occurred almost immediately after the end of farming,peaking about 10–20 years later,with an earlier start in the Hruby Jeseník Mountains.The succession led to the significant change of land use/land cover and these processes were similar in both mountain ranges.The largest changes were a decrease in grasslands by 62%–64%and an increase in forest area by 33%–40%for both study areas.The abandonment of land is the main consequence of a crucial political driving forces(displacement of German-speaking population)in the Krkono?e Mountains and the Hruby Jeseník Mountains.
基金funded by the Gansu Provincial Department of Education's University Teacher Innovation Fund Project(2025A-001)the Gansu Provincial Philosophy and Social Science Planning Project(2024YB088).
文摘Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development,accelerating the achievement of the"dual carbon"goals,and promoting the high-quality development of ecological environment.The carbon sequestration capacity within the mountain-desert-oasis system(MDOS),a unique landscape pattern,exhibits significant gradient characteristics,and its carbon sink potential can be substantially improved through multi-scale spatial optimization.This study employed the Integrated Valuation of Ecosystem Services and Tradeoff(InVEST)model to estimate carbon storage and sequestration(CSS)in the Gansu section of Heihe River Basin,China,a representative MDOS,based on land use/land cover(LULC)data from 1990 to 2020.The Patch-level Land Use Simulation(PLUS)model was coupled to simulate LULC and estimate carrying CSS under natural development(ND),ecological protection(EP),water constraint(WC),and economic development(ED)scenarios for 2035.Furthermore,the study constructed and optimized the CSS pattern on the basis of economic and ecological benefits,exploring the guiding significance of different scenarios for pattern optimization.The results showed that CSS spatial distribution is closely correlated with LULC pattern,and CSS is expected to improve in the future.CSS showed an overall increase across subsystems during 1990–2020,but varied across LULC types.CSS of construction land in all subsystems exhibited an increasing trend,while CSS of unused land showed a decreasing trend,with specific changes of 1.68×103 and 3.43×105 t,respectively.Regional CSS dynamics were mainly driven by conversions among unused land,cultivated land,and grassland.The CSS pattern of MDOS was divided into carbon sink functional region(CSFR),low carbon conservation region(LCCR),low carbon economic region(LCER),and economic development region(EDR).Water resources coordination served as the basis of pattern optimization,while the four dimensions—ecological carbon sink,low-carbon maintenance,agricultural carbon reduction and sink enhancement,and urban carbon emission reduction—framed the optimization framework.ND,EP,WC,and ED scenarios provided guidance as the basic reference,optimal benefit,"dual carbon"baseline,and upper development limit,respectively.Additionally,the detailed CSS sub-partitions of MDOS covered most potential scenarios of such ecosystems,demonstrating the applicability of these sub-partitions.These findings provide valuable references for enhancing CSS and hold important significance for low-carbon territorial spatial planning in the MDOS.
基金supported by the National Key R&D Program of China(2022YFD1900503).
文摘Carbon storage serves as a key indicator of ecosystem services and plays a vital role in maintaining the global carbon balance.Land use and cover change(LUCC)is one of the primary drivers influencing carbon storage variations in terrestrial ecosystems.Therefore,evaluating the impacts of LUCC on carbon storage is crucial for achieving strategic goals such as the China’s dual carbon goals(including carbon peaking and carbon neutrality).This study focuses on the Aral Irrigation Area in Xinjiang Uygur Autonomous Region,China,to assess the impacts of LUCC on regional carbon storage and their spatiotemporal dynamics.A comprehensive LUCC database from 2000 to 2020 was developed using Landsat satellite imagery and the random forest classification algorithm.The integrated valuation of ecosystem services and trade-offs(InVEST)model was applied to quantify carbon storage and analyze its response to LUCC.Additionally,future LUCC patterns for 2030 were projected under multiple development scenarios using the patch-generating land use simulation(PLUS)model.These future LUCC scenarios were integrated with the InVEST model to simulate carbon storage trends under different land management pathways.Between 2000 and 2020,the dominant land use types in the study area were cropland(area proportion of 35.52%),unused land(34.80%),and orchard land(12.19%).The conversion of unused land and orchard land significantly expanded the area of cropland,which increased by 115,742.55 hm^(2).During this period,total carbon storage and carbon density increased by 7.87×10^(6) Mg C and 20.19 Mg C/hm^(2),respectively.The primary driver of this increase was the conversion of unused land into cropland,accounting for 49.28%of the total carbon storage gain.Carbon storage was notably lower along the northeastern and southeastern edges.By 2030,the projected carbon storage is expected to increase by 0.99×10^(6),1.55×10^(6),and 1.71×10^(6) Mg C under the natural development,cropland protection,and ecological conservation scenarios,respectively.In contrast,under the urban development scenario,carbon storage is projected to decline by 0.40×10^(6) Mg C.In line with China’s dual carbon goals,the ecological conservation scenario emerges as the most effective strategy for enhancing carbon storage.Accordingly,strict enforcement of the cropland red line is recommended.This study provides a valuable scientific foundation for regional ecosystem restoration and sustainable development in arid regions.
基金funded by the Liaoning Provincial Social Science Planning Fund(L22AYJ010).
文摘The Liaohe River Basin(LRB)in Northeast China,a critical agricultural and industrial zone,has faced escalating water resource pressures in recent decades due to rapid urbanization,intensified land use changes,and climate variability.Understanding the spatiotemporal dynamics of water yield and its driving factors is essential for sustainable water resource management in this ecologically sensitive region.This study employed the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model to quantify the spatiotemporal patterns of water yield in the LRB(dividing into six sub-basins from east to west:East Liaohe River Basin(ELRB),Taizi River Basin(TRB),Middle Liaohe River Basin(MLRB),West Liaohe River Basin(WLRB),Xinkai River Basin(XRB),and Wulijimuren River Basin(WRB))from 1993 to 2022,with a focus on the impacts of climate change and land use cover change(LUCC).Results revealed that the LRB had an average annual precipitation of 483.15 mm,with an average annual water yield of 247.54 mm,both showing significant upward trend over the 30-a period.Spatially,water yield demonstrated significant heterogeneity,with higher values in southeastern sub-basins and lower values in northwestern sub-basins.The TRB exhibited the highest water yield due to abundant precipitation and favorable topography,while the WRB recorded the lowest water yield owing to arid conditions and sparse vegetation.Precipitation played a significant role in shaping the annual fluctuations and total volume of water yield,with its variability exerting substantially greater impacts than actual evapotranspiration(AET)and LUCC.However,LUCC,particularly cultivated land expansion and grassland reduction,significantly reshaped the spatial distribution of water yield by modifying surface runoff and infiltration patterns.This study provides critical insights into the spatiotemporal dynamics of water yield in the LRB,emphasizing the synergistic effects of climate change and land use change,which are pivotal for optimizing water resource management and advancing regional ecological conservation.
基金National Natural Science Foundation of China,No.52379053,No.52022108The Key Research Project of Science and Technology in Inner Mongolia Autonomous Region of China,No.NMKJXM202208,No.NMKJXM202301The Project Funded by the Water Resources Department of Inner Mongolia Autonomous Region of China,No.NSK202103。
文摘Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classification(SPLC)method combined with a fusion model(flexible spatiotemporal data fusion,FSDAF)(abbreviated as SPLC-F)was proposed to map multi-year land cover and crop type(LC-CT)distribution in agricultural irrigated areas with complex landscapes and cropping system,using time series optical images(Landsat and MODIS).The SPLC-F method was well validated and applied in a super-large irrigated area(Hetao)of the upper Yellow River Basin(YRB).Results showed that the SPLC-F method had a satisfactory performance in producing long-term LC-CT maps in Hetao,without the requirement of field sampling.Then,the spatio-temporal variation and the driving factors of the cropping systems were further analyzed with the aid of detailed household surveys and statistics.We clarified that irrigation and salinity conditions were the main factors that had impacts on crop spatial distribution in the upper YRB.Investment costs,market demand,and crop price are the main driving factors in determining the temporal variations in cropping distribution.Overall,this study provided essential multi-year LC-CT maps for sustainable management of agriculture,eco-environments,and food security in the upper YRB.
基金supported by Geological survey project of China Geological Survey(DD20230481,DD20242461)。
文摘Terrain and geological formation are crucial natural environmental factors that constrain land use and land cover changes.Studying their regulatory role in regional land use and land cover changes is significant for guiding regional land resource management.Taking the Danjiang River Basin in the Qinling Mountains of China as an example,this paper incorporates terrain(elevation,slope,and aspect)factors and geological formation to comprehensively analyse the differentiation characteristics of land use spatial patterns based on the examination of land use changes in 2000,2010,and 2020.Moreover,the geographical detector is employed to compare and analyse the effect of each factor on the spatial heterogeneity of land use.The results show that:(1)From 2000 to 2020,the areas of arable land and forestland in the Danjiang River Basin decreased while the areas of grassland,water areas,construction land,and unused land continuously increased.The comprehensive land use dynamics index was+0.09%,indicating a generally low level of land development.(2)Differences in the natural environmental factors of terrain and geological formation have a significant controlling effect on the spatial heterogeneity of land use.Specifically,there are notable differences in the advantageous distribution characteristics of various land use types across different levels of influencing factors.(3)The factor detection results reveal that geological formation has the strongest influence on the spatial heterogeneity of land use,followed by elevation and slope while aspect has the weakest influence.After the interaction among the factors,they nonlinearly enhance the explanation of spatial heterogeneity in land use.Overall,the influence of geological formation on the spatial heterogeneity of land use is greater than that of terrain factors.This study provides new geological evidence for natural resource management departments to conduct regional spatial planning,ecological and environmental protection and restoration,and land structure optimization and adjustment.
基金supported by the Central Government to Guide Local Technological Development(23ZYQH0298)the Science and Technology Project of Gansu Province(20JR10RA656,22JR5RA416)the Science and Technology Project of Wuwei City(WW2202YFS006).
文摘Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.
文摘Cloud cover constitutes a major obstacle to land cover classification in the humid tropical regions when using optical remote sensing such as Landsat imagery. The advent of freely available Sentinel-1 C band synthetic aperture radar (SAR) imagery offers new opportunities for land cover classification in frequently cloud covered environments. In this study, we investigated the utility of Sentinel-1 for extracting land use land cover (LULC) information in the coastal low lying strip of Douala, Cameroon when compared with Landsat enhanced thematic mapper (TM). We also assessed the potential of integrating Sentinel-1 and Landsat. The major LULC classes in the region included water, settlement, bare ground, dark mangroves, green mangroves, swampy vegetation, rubber, coastal forest and other vegetation and palms. Textural variables including mean, correlation, contrast and entropy were derived from the Sentinel-1 C band. Various conventional image processing techniques and the support vector machine (SVM) algorithm were applied. Only four land cover classes (settlement, water, mangroves and other vegetation and rubber) could be calibrated and validated using SAR imagery due to speckles. The Sentinel-1 only classification yielded a lower overall classification accuracy (67.65% when compared to all Landsat bands (88.7%)). The integrated Sentinel-1 and Landsat data showed no significant differences in overall accuracy assessment (88.71% and 88.59%, respectively). The three best spectral bands (5, 6, 7) of Landsat imagery yielded the highest overall accuracy assessment (91.96%). in the study. These results demonstrate a lower potential of Sentinel-1 for land cover classification in the Douala estuary when compared with cloud free Landsat images. However, comparable results were obtained when only broad classes were considered.
基金supported by the National Natural Science Foundation of China(Grant No.31670473)the Wuhan Institute of Technology funding to Dr.Siyue Li(Grant No.21QD02).
文摘Riparian land use/land cover(LULC)plays a crucial role in maintaining riverine water quality by altering the transport of pollutants and nutrients.Nevertheless,establishing a direct relationship between water quality and LULC is challenging due to the multi-indicator nature of both factors.Water quality encompasses a multitude of physical,chemical,and biological parameters,while LULC represents a diverse array of land use types.Riparian habitat quality(RHQ)serves as an indicator of LULC.Yet,it remains to be seen whether RHQ can act as a proxy of LULC for assessing the impact of LULC on riverine water quality.This study examines the interplay between RHQ,LULC and water quality,and develops a comprehensive indicator to predict water quality.We measured several water quality parameters,including pH(potential of hydrogen),TN(total nitrogen),TP(total phosphorus),T_(water)(water temperature),DO(dissolved oxygen),and EC(electrical conductivity)of the Yue and Jinshui Rivers draining to the Han River during 2016,2017 and 2018.The water quality index(WQI)was further calculated.RHQ is assessed by the InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)model.Our study found noticeable seasonal differences in water quality,with a higher WQI observed in the dry season.The RHQ was strongly correlated with LULC compositions.RHQ positively correlated with WQI,and DO concentration and vegetation land were negatively correlated with T_(water),TN,TP,EC,cropland,and construction land.These correlations were stronger in the rainy season.Human-dominated land,such as construction land and cropland,significantly contributed to water quality degradation,whereas vegetation promoted water quality.Regression models showed that the RHQ explained variations in WQI better than LULC types.Our study concludes that RHQ is a new and comprehensive indicator for predicting the dynamics of riverine water quality.
基金supported by the Third Xinjiang Scientific Expedition Program (2022xjkk1100)the Tianchi Talent Project
文摘The Turpan-Hami(Tuha)Basin in Xinjiang Uygur Autonomous Region of China,holds significant strategic importance as a key economic artery of the ancient Silk Road and the Belt and Road Initiative,necessitating a holistic understanding of the spatiotemporal evolution of land use/land cover(LULC)to foster sustainable planning that is tailored to the region's unique resource endowments.However,existing LULC classification methods demonstrate inadequate accuracy,hindering effective regional planning.In this study,we established a two-level LULC classification system(8 primary types and 22 secondary types)for the Tuha Basin.By employing Landsat 5/7/8 imagery at 5-a intervals,we developed the LULC dataset of the Tuha Basin from 1990 to 2020,conducted the accuracy assessment and spatiotemporal evolution analysis,and simulated the future LULC under various scenarios via the Markov-Future Land Use Simulation(Markov-FLUS)model.The results revealed that the average overall accuracy values of our LULC dataset were 0.917 and 0.864 for the primary types and secondary types,respectively.Compared with the seven mainstream LULC products(GlobeLand30,Global 30-meter Land Cover with Fine Classification System(GLC_FCS30),Finer Resolution Observation and Monitoring of Global Land Cover PLUS(FROM_GLC PLUS),ESA Global Land Cover(ESA_LC),Esri Land Cover(ESRI_LC),China Multi-Period Land Use Land Cover Change Remote Sensing Monitoring Dataset(CNLUCC),and China Annual Land Cover Dataset(CLCD))in 2020,our LULC data exhibited dramatically elevated overall accuracy and provided more precise delineations for land features,thereby yielding high-quality data backups for land resource analyses within the basin.In 2020,unused land(78.0%of the study area)and grassland(18.6%)were the dominant LULC types of the basin;although cropland and construction land constituted less than 1.0%of the total area,they played a vital role in arid land development and primarily situated within oases that form the urban cores of the cities of Turpan and Hami.Between 1990 and 2020,cropland and construction land exhibited a rapid expansion,and the total area of water body decreased yet resurging after 2015 due to an increase in areas of reservoir and pond.In future scenario simulations,significant increases in areas of construction land and cropland are anticipated under the business-as-usual scenario,whereas the wetland area will decrease,suggesting the need for ecological attention under this development pathway.In contrast,the economic development scenario underscores the fast-paced expansion of construction land,primarily from the conversion of unused land,highlighting the significant developmental potential of unused land with a slowing increase in cropland.Special attention should thus be directed toward ecological and cropland protection during development.This study provides data supports and policy recommendations for the sustainable development goals of Tuha Basin and other similar arid areas.
基金supported by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams(2021ZT090543)the National Natural Science Foundation of China(U20A20117)the Key-Area Research and Development Program of Guangdong Province(2020B1111380003).
文摘Land use/land cover represents the interactive and comprehensive influences between human activities and natural conditions,leading to potential conflicts among natural and human-related issues as well as among stakeholders.This study introduced economic standards for farmers.A hybrid approach(CA-ABM)of cellular automaton(CA)and an agent-based model(ABM)was developed to effectively deal with social and land-use synergic issues to examine human–environment interactions and projections of land-use conversions for a humid basin in south China.Natural attributes and socioeconomic data were used to analyze land use/land cover and its drivers of change.The major modules of the CA-ABM are initialization,migration,assets,land suitability,and land-use change decisions.Empirical estimates of the factors influencing the urban land-use conversion probability were captured using parameters based on a spatial logistic regression(SLR)model.Simultaneously,multicriteria evaluation(MCE)and Markov models were introduced to obtain empirical estimates of the factors affecting the probability of ecological land conversion.An agent-based CA-SLR-MCE-Markov(ABCSMM)land-use conversion model was proposed to explore the impacts of policies on land-use conversion.This model can reproduce observed land-use patterns and provide links for forest transition and urban expansion to land-use decisions and ecosystem services.The results demonstrated land-use simulations under multi-policy scenarios,revealing the usefulness of the model for normative research on land-use management.
基金National Natural Science Foundation of China(Nos.42371406,42071441,42222106,61976234).
文摘With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.
文摘As the global population continues to expand,the demand for natural resources increases.Unfortunately,human activities account for 23%of greenhouse gas emissions.On a positive note,remote sensing technologies have emerged as a valuable tool in managing our environment.These technologies allow us to monitor land use,plan urban areas,and drive advancements in areas such as agriculture,climate changemitigation,disaster recovery,and environmentalmonitoring.Recent advances in Artificial Intelligence(AI),computer vision,and earth observation data have enabled unprecedented accuracy in land use mapping.By using transfer learning and fine-tuning with red-green-blue(RGB)bands,we achieved an impressive 99.19%accuracy in land use analysis.Such findings can be used to inform conservation and urban planning policies.
文摘The Himalayan region has been experiencing stark impacts of climate change,demographic and livelihood pattern changes.The analysis of land use and land cover(LULC)change provides insights into the shifts in spatial and temporal patterns of landscape.These changes are the combined effects of anthropogenic and natural/climatic factors.The present study attempts to monitor and comprehend the main drivers behind LULC changes(1999-2021)in the Himalayan region of Pithoragarh district,Uttarakhand.Pithoragarh district is a border district,remotely located in the north-east region of Uttarakhand,India.The study draws upon primary and secondary data sources.A total of 400 household surveys and five group discussions from 38 villages were conducted randomly to understand the climate perception of the local community and the drivers of change.Satellite imagery,CRU(Climatic Research Unit)climate data and climate perception data from the field have been used to comprehensively comprehend,analyze,and discuss the trends and reasons for LULC change.GIS and remote sensing techniques were used to construct LULC maps.This multifaceted approach ensures comprehensive and corroborated information.Five classes were identified and formed viz-cultivation,barren,settlement,snow,and vegetation.Results show that vegetation and builtup have increased whereas cultivation,barren land,and snow cover have decreased.The study further aims to elucidate the causes behind LULC changes in the spatially heterogeneous region,distinguishing between those attributed to human activities,climate shifts,and the interconnected impacts of both.The study provides a comprehensive picture of the study area and delivers a targeted understanding of local drivers and their potential remedies by offering a foundation for formulating sustainable adaptation policies in the region.
基金partly funded by the National Key Research and Development Program of China(NK2023190801)the National Foreign Experts Program of China(G2023041024L)the Key Scientific Research Program of Shaanxi Provincial Education Department,China(21JT028)。
文摘Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and transfer rate of LULC in the Jinghe River Basin(JRB),China using LULC data from 2000 to 2020.Through trajectory analysis,knowledge maps,chord diagrams,and standard deviation ellipse method,we examined the spatiotemporal characteristics of LULC changes.We further established an index system encompassing natural factors(digital elevation model(DEM),slope,aspect,and curvature),socio-economic factors(gross domestic product(GDP)and population),and accessibility factors(distance from railways,distance from highways,distance from water,and distance from residents)to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector(Geodetector).The key findings indicate that from 2000 to 2020,the JRB experienced significant LULC changes,particularly for farmland,forest,and grassland.During the study period,LULC change trajectories were categorized into stable,early-stage,late-stage,repeated,and continuous change types.Besides the stable change type,the late-stage change type predominated the LULC change trajectories,comprising 83.31% of the total change area.The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010.The LULC changes exhibited a discrete spatial expansion trend during 2000-2020,predominantly extending from southeast to northwest of the JRB.Influential driving factors on LULC changes included slope,GDP,and distance from highways.The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020.This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.
